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Extended Isomap for classification | IEEE Conference Publication | IEEE Xplore
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Extended Isomap for classification


Abstract:

The Isomap method has demonstrated promising results in finding a low dimensional embedding from samples in the high dimensional input space. The crux of this method is t...Show More

Abstract:

The Isomap method has demonstrated promising results in finding a low dimensional embedding from samples in the high dimensional input space. The crux of this method is to estimate geodesic distance with multidimensional scaling for dimensionality reduction. Since the Isomap method is developed based on the reconstruction principle, it may not be optimal from the classification viewpoint. We present an extended Isomap method that utilizes the Fisher linear discriminant for pattern classification. Numerous experiments on image data sets show that our extension is more effective than the original Isomap method for pattern classification. Furthermore, the extended Isomap shows promising results compared with best classification methods in the literature.
Date of Conference: 11-15 August 2002
Date Added to IEEE Xplore: 10 December 2002
Print ISBN:0-7695-1695-X
Print ISSN: 1051-4651
Conference Location: Quebec City, QC, Canada

References

References is not available for this document.